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A recommendation algorithm is typically applied to speculate on users’ preferences based on their behavioral characteristics. The purpose of this paper is to provide a systematic review of recommendation systems by collecting related journal articles from the last five years (i.e. from 2014 to 2018). This paper aims to study the correlations between recommendation technologies and e-learning systems.

The paper reviews the relevant articles using five assessment aspects. A coding scheme was put forward that includes the following: the metrics for the e-learning system, the evaluation metrics for the recommendation algorithms, the recommendation filtering technology, the phases of the recommendation process and the learning outcomes of the system.

The research indicates that most e-learning systems will adopt the adaptive mechanism as a primary metric, and accuracy is a vital evaluation indicator for recommendation algorithms. In existing e-learning recommender systems, the most common recommendation filtering technology is hybrid filtering. The information collection phase is an important process recognized by most studies. Finally, the learning outcomes of the recommender system can be achieved through two key indicators: affections and correlations.

The recommendation technology works effectively in closing the gap between the information producer and the information consumer. This technology could help learners find the information they are interested in as well as send them a valuable message. The opportunities and challenges of the current study are discussed; the results of this study could provide a guideline for future research.

This article proposes an architecture of an intelligent and autonomous recommendation system to be applied to any virtual learning environment, with the objective of efficiently recommending digital resources. The paper presents the architectural details of the intelligent and autonomous dimensions of the recommendation system. The paper describes a hybrid recommendation model that orchestrates and manages the available information and the specific recommendation needs, in order to determine the recommendation algorithms to be used. The hybrid model allows the integration of the approaches based on collaborative filter, content or knowledge. In the architecture, information is extracted from four sources: the context, the students, the course and the digital resources, identifying variables, such as individual learning styles, socioeconomic information, connection characteristics, location, etc. Tests were carried out for the creation of an academic course, in order to analyse the intelligent and autonomous capabilities of the architecture.

Recommendation systems are widely used in tourism in order to provide people personalized suggestions that would make their trip memorable. Nowadays, mobile assisted guided tours based on recommendation services are used in museums to enhance visitors ’ experience. However, all those systems have been designed to target indoor or outdoor museum visits. Is it feasible to design a system that supports mobile services that connect a museum visit to artworks situated outdoor in the city environment? Is it possible to connect the artworks of a city center to the exhibits of a museum? In this work, we attempt to give a first answer to such questions proposing and implementing a set of services that connects the museum to the city public space. In order to show the strength of the implemented services, we present a basic usage scenario along with a first system evaluation showing positive results.

In the past few years, millions of people started to acquire knowledge from the Massive Open Online Courses (MOOCs). MOOCs contain massive video courses produced by instructors, and learners all over the world can get access to these courses via the internet. However, faced with massive courses, learners often waste much time finding courses they like. This paper aims to explore the problem that how to make accurate personalized recommendations for MOOC users.

This paper proposes a multi-attribute weight algorithm based on collaborative filtering (CF) to select a recommendation set of courses for target MOOC users.

The recall of the proposed algorithm in this paper is higher than both the traditional CF and a CF-based algorithm – uncertain neighbors’ collaborative filtering recommendation algorithm. The higher the recall is, the more accurate the recommendation result is.

This paper reflects the target users’ preferences for the first time by calculating separately the weight of the attributes and the weight of attribute values of the courses.

Science policy and practice for open access (OA) books is a rapidly evolving area in the scholarly domain. However, there is much that remains unknown, including how many OA books there are and to what degree they are included in preservation coverage. The purpose of this study is to contribute towards filling this knowledge gap in order to advance both research and practice in the domain of OA books.

This study utilized open bibliometric data sources to aggregate a harmonized dataset of metadata records for OA books (data sources: the Directory of Open Access Books, OpenAIRE, OpenAlex, Scielo Books, The Lens, and WorldCat). This dataset was then cross-matched based on unique identifiers and book titles to openly available content listings of trusted preservation services (data sources: Cariniana Network, CLOCKSS, Global LOCKSS Network, and Portico). The web domains of the OA books were determined by querying the web addresses or digital object identifiers provided in the metadata of the bibliometric database entries.

In total, 396,995 unique records were identified from the OA book bibliometric sources, of which 19% were found to be included in at least one of the preservation services. The results suggest reason for concern for the long tail of OA books distributed at thousands of different web domains as these include volatile cloud storage or sometimes no longer contained the files at all.

Data quality issues, varying definitions of OA across services and inconsistent implementation of unique identifiers were discovered as key challenges. The study includes recommendations for publishers, libraries, data providers and preservation services for improving monitoring and practices for OA book preservation.

This study provides methodological and empirical findings for advancing the practices of OA book publishing, preservation and research.

This qualitative research set out to understand what teachers’ assessments were of the context of teaching as it relates to the curriculum, and what they consider appropriate for an optimal teaching and learning experience in a university English language teaching (ELT) context.

Qualitative data were deemed required to understand the effects and understanding teachers had of the ELT curriculum as it played out in their teaching context. Focus group interviews and observations were the main method for data generation.

The context has a bearing on the ongoing development of teachers’ intercultural sensitivity (IS) frames and how they address IS over time in their context of teaching as it pertains to curriculum.

This is an original research paper which gives insight to knowledge about the relationship between ELT, curriculum and culture.

The paper examines academic historians' information interactions with material from digital historical-newspaper collections as the research process unfolds.

The study employed qualitative analysis from in-depth interviews with Finnish history scholars who use digitised historical newspapers as primary sources for their research. A model for task-based information interaction guided the collection and analysis of data.

The study revealed numerous information interactions within activities related to task-planning, the search process, selecting and working with the items and synthesis and reporting. The information interactions differ with the activities involved, which call for system support mechanisms specific to each activity type. Various activities feature information search, which is an essential research method for those using digital collections in the compilation and analysis of data. Furthermore, application of quantitative methods and multidisciplinary collaboration may be shaping culture in history research toward convergence with the research culture of the natural sciences.

For sustainable digital humanities infrastructure and digital collections, it is of great importance that system designers understand how the collections are accessed, why and their use in the real-world context. The study enriches understanding of the collections' utilisation and advances a theoretical framework for explicating task-based information interaction.

Recommender systems collect information about users and businesses and how they are related. Such relation is given in terms of reviews and votes on reviews. User reviews gather opinions, rating scores and review influence. The latter component is crucial for determining which users are more relevant in a recommender system, that is, the users whose reviews are more popular than the average user’s reviews.

A model of measure of user influence is proposed based on review and social attributes of the user. User influence is also used for determining how influenced has been a business being based on popular reviews.

Results indicate there is a connection between social attributes and user influence. Such results are relevant for marketing, credibility estimation and Sybil detections, among others.

The proposed model allows search parameterization based on the social attribute weights of users, reviews and businesses. Such weights defines the relevance on each attribute, which can be adjusted according to the search needs. Popularity results are then a function of weight preferences on user, reviews and businesses data join.